{"id":"W2118670046","doi":"10.1002/sim.2518","title":"Confidence intervals for multinomial logistic regression in sparse data","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Multinomial logistic regression; Statistics; Logistic regression; Mathematics; Covariate; Confidence interval; Likelihood function; Binary data; Multinomial distribution; Econometrics; Wald test; Maximum likelihood; Binary number; Statistical hypothesis testing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05745259,0.001018041,0.002248483,0.005898071,0.0007125,0.003082971,0.003726707,0.002918353,0.00420864],"category_scores_gemma":[0.4696358,0.0006826512,0.001740878,0.004688104,0.002545983,0.00452929,0.003582222,0.004100611,0.0007643561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001394151,"about_ca_system_score_gemma":0.0009957701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001782368,"about_ca_topic_score_gemma":0.0008083042,"domain_scores_codex":[0.9628756,0.02480062,0.002210967,0.003397619,0.006172425,0.0005428099],"domain_scores_gemma":[0.473363,0.4768979,0.01956654,0.01686404,0.01168451,0.001623986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001093319,0.0001507822,0.03100645,0.002111248,0.001186032,0.0008017079,0.001130146,0.157902,0.001383544,0.4576526,0.009322716,0.3362594],"study_design_scores_gemma":[0.0001945462,0.0002539938,0.01108448,0.001609459,0.0002884482,0.001294657,0.0003165603,0.5516371,0.00216,0.4203793,0.01059385,0.0001876475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01823949,0.004447138,0.9726778,0.0008716058,0.0001225246,0.000112785,0.0004732514,0.0006581369,0.002397236],"genre_scores_gemma":[0.5197155,0.003546472,0.4709294,0.0005690887,0.0004096444,0.001176246,0.002331793,0.0003409926,0.0009807787],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05745259,"threshold_uncertainty_score":0.3038419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2664078337446776,"score_gpt":0.4945271978202856,"score_spread":0.228119364075608,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}